Predicting Ag-archiving Capacity
Ryan Sheridan
2023-11-08
Summary
- The goal of this project is to use machine learning to predict Ag archiving capacity (or ability) in LECs from naive mice.
- For a proof of principle analysis, Ag-tracking data for d14 cLECs was used to train a random forest classifier to predict Ag status
- Using this model we defined a gene program that correlates with Ag status at various timepoints
- Archiving “competent” cLECs can be predicted in the CHIKV LN scRNA-seq data
- There is a reduction in archiving-competent cLECs in CHIKV-infected mice and a broad downregulation of the Ag-archiving gene program
- The central goal for this project is to optimize the model (e.g. expand to other cell types) and use it to assess archiving capacity in samples that did not receive an Ag-tag (e.g. other published datasets). We can then identify perturbations/treatments etc that are predicted to impair archiving.
Ag signal is shown below for broad cell types identified in the CD45- dataset.
Ag signal is shown below for day 14 LEC subsets.
Ag signal is shown below for cLEC subsets for each timepoint.
Classifying Ag-high
Ag-low and -high cells were identified by separately clustering each LEC subset for each sample into two groups based on Ag-score. For the 6wk-3wk sample, the 3wk Ag score is used. Ag-low/high classifications used for the analysis are shown below.
Ag-low and -high cells are shown below for cLECs.
Ag-low and -high cells are shown for d14 LEC subsets
A random forest classifier was trained using data for d14 cLECs. The model was then used to predict Ag-high cells in the other Ag datasets.
The fraction of cells belonging to each predicted Ag group is shown on the left for cLECs from each sample. The fraction of true Ag-low, true Ag-high, and false-positive Ag-high cells (high-pred) is shown on the right.
- The model is fairly accurate in predicting Ag-high cells in the training and test data (d14 cLECs), but does not perform as well when predicting Ag-low cells, this can be improved with more optimization
- Since we want to identify gene signatures that are expressed in naive mice and continue to be expressed after Ag levels have fallen, we expect to observe an increasing fraction of false positive Ag-high cells for the later timepoints.
Model accuracy was assessed for each LEC subset. F1 scores are shown for different combinations of testing and training data.
- Ag-high cLECs, collecting, and fLECs are easiest to predict. All models show high F1 scores when tested using these LEC subsets.
- Ag-high Ptx3 LECs and BECs are most difficult to accurately predict. This is expected since these cell populations have the lowest Ag signal and the fewest Ag-high cells.
- The F1 score is not always highest when the models are tested using the training cell type. This is not necessarily surprising since the models were selected using several metrics in addition to the F1 score.
F1 scores are shown for the d14 timepoint using testing and training data from the same LEC subset.
Ag modules
Expression of the top upregulated (top) and downregulated (bottom) gene modules that are most predictive of Ag signal are shown below.
- There is a notable correlation between the expression of these genes and the Ag class
- False positive Ag high cells (high-pred) show an intermediate level of expression that falls roughly between the true Ag-low and true Ag-high cells.
- The false positive Ag high cells are potentially cells that are archiving-competent but have now lost/released most Ag at the later timepoints
Expression of the Ag-high and -low gene modules is shown below for 14, 21, and 42 day timepoints.
The cLEC Ag-high gene module is shown below.
UMAP projections show Ag-high module expression (top), true Ag-low vs true Ag-high (middle), and false-positive Ag-high (high-pred) vs true Ag-high (bottom).
- False positive Ag-high cells (high-pred) show strong overlap with true Ag-high cells
cLEC
